Ranking and selecting entities based on calculated reputation or influence scores

ABSTRACT

Ranking and selecting entities based on calculated reputation or influence scores is provided. In some embodiments, a method includes determining whether a first entity is a subject or an object; determining whether a second entity is a subject or an object; and generating a graph, in which a subset of the graph is a subject graph of subject nodes that includes at least one or more subjects (e.g., subject entities) linked to one or more other subjects, and in which the graph includes one or more objects (e.g., object entities) each linked to one or more subjects in the subject graph. In some embodiments, the graph includes directed and undirected links. In some embodiments, the graph includes one or more objects linked to one or more objects.

CROSS REFERENCE TO OTHER APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 61/200,664 entitled SYSTEM AND METHOD OF RANKING AND SELECTINGENTITIES BASED ON CALCULATED REPUTATION OR INFLUENCE SCORES filed Dec.1, 2008, which is incorporated herein by reference for all purposes.This application is also a continuation-in-part of U.S. Ser. No.11/809,489, filed Jun. 1, 2007 (now U.S. Pat. No. 7,831,536).

BACKGROUND OF THE INVENTION

Knowledge is increasingly more germane to our exponentially expandinginformation-based society. Perfect knowledge is the ideal thatparticipants seek to assist in decision making and for determiningpreferences, affinities, and dislikes. Practically, perfect knowledgeabout a given topic is virtually impossible to obtain unless theinquirer is the source of all of information about such topic (e.g.,autobiographer). Armed with more information, decision makers aregenerally best positioned to select a choice that will lead to a desiredoutcome/result (e.g., which restaurant to go to for dinner). However, asmore information is becoming readily available through variouselectronic communications modalities (e.g., the Internet), one is leftto sift through what is amounting to a myriad of data to obtain relevantand, more importantly, trust worthy information to assist in decisionmaking activities. Although there are various tools (e.g., searchengines, community boards with various ratings), there lacks any indiciaof personal trustworthiness (e.g., measure of the source's reputationand/or influence) with located data.

SUMMARY OF THE INVENTION

A new graph is proposed wherein each node represents either a subject,which can be a user, or an object, which can be a content item cited bythe user, and each edge represents a citation, which is an expression ofopinion or description by a user on an object. Additionally, a newmethod is proposed for generating and ranking search result based onmatching of the search terms with the content of the citations of theobjects by the users.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of the invention are disclosed in the followingdetailed description and the accompanying drawings.

FIG. 1 is an illustrative model of the World Wide Web showingindividuals linked to other individuals and to documents they author.

FIG. 2 is a block diagram showing the cooperation of exemplarycomponents of another illustrative implementation in accordance withsome embodiments.

FIG. 3 is a block diagram showing an illustrative block representationof an illustrative system in accordance with some embodiments.

FIG. 4 is a block diagram describing the interaction of various partiesof an exemplary referral environment in accordance with someembodiments.

FIG. 5 is a block diagram of the search space of an exemplary referralenvironment in accordance with some embodiments.

FIG. 6 is a flow diagram showing illustrative processing performed ingenerating referrals in accordance with some embodiments.

DETAILED DESCRIPTION

The invention can be implemented in numerous ways, including as aprocess; an apparatus; a system; a composition of matter; a computerprogram product embodied on a computer readable storage medium; and/or aprocessor, such as a processor configured to execute instructions storedon and/or provided by a memory coupled to the processor. In thisspecification, these implementations, or any other form that theinvention may take, may be referred to as techniques. In general, theorder of the steps of disclosed processes may be altered within thescope of the invention. Unless stated otherwise, a component such as aprocessor or a memory described as being configured to perform a taskmay be implemented as a general component that is temporarily configuredto perform the task at a given time or a specific component that ismanufactured to perform the task. As used herein, the term ‘processor’refers to one or more devices, circuits, and/or processing coresconfigured to process data, such as computer program instructions.

A detailed description of one or more embodiments of the invention isprovided below along with accompanying figures that illustrate theprinciples of the invention. The invention is described in connectionwith such embodiments, but the invention is not limited to anyembodiment. The scope of the invention is limited only by the claims andthe invention encompasses numerous alternatives, modifications andequivalents. Numerous specific details are set forth in the followingdescription in order to provide a thorough understanding of theinvention. These details are provided for the purpose of example and theinvention may be practiced according to the claims without some or allof these specific details. For the purpose of clarity, technicalmaterial that is known in the technical fields related to the inventionhas not been described in detail so that the invention is notunnecessarily obscured.

Currently, a person seeking to locate information to assist in adecision, to determine an affinity, and/or identify a dislike canleverage traditional non-electronic data sources (e.g., personalrecommendations—which can be few and can be biased) and/or electronicdata sources such as web sites, bulletin boards, blogs, and othersources to locate (sometimes rated) data about a particulartopic/subject (e.g., where to stay when visiting San Francisco). Such anapproach is time consuming and often unreliable as with most of theelectronic data there lacks an indicia of trustworthiness of the sourceof the information. Failing to find a plethora (or spot on) informationfrom immediate non-electronic and/or electronic data source(s), theperson making the inquiry is left to make the decision using limitedinformation, which can lead to less than perfect predictions ofoutcomes, results, and can lead to low levels of satisfactionundertaking one or more activities for which information was sought.

Current practices also do not leverage trustworthiness of informationor, stated differently, attribute a value to the reputation of thesource of data (e.g., referral). With current practices, the entityseeking the data must make a value judgment on the reputation of thedata source. Such value judgment is generally based on previousexperiences with the data source (e.g., rely on Mike's restaurantrecommendations as he is a chef and Laura's hotel recommendations inEurope as she lived and worked in Europe for 5 years). Unless the personmaking the inquiry has an extensive network of references from which torely to obtain desired data needed to make a decision, most often, theperson making the decision is left to take a risk or “roll the dice”based on best available non-attributed (non-reputed) data. Such aprospect often leads certain participants from not engaging in acontemplated activity.

Reputation accrued by persons in such a network of references aresubjective. In other words, reputation accrued by persons in such anetwork of references appear differently to each other person in thenetwork, as each person's opinion is formed by their own individualnetworks of trust.

Real world trust networks follow a small-world pattern, that is, whereeveryone is not connected to everyone else directly, but most people areconnected to most other people through a relatively small number ofintermediaries or “connectors”. Accordingly, this means that someindividuals within the network may disproportionately influence theopinion held by other individuals. In other words, some people'sopinions may be more influential than other people's opinions.

In some embodiments, influence is provided for augmenting reputation,which may be subjective. In some embodiments, influence is provided asan objective measure. For example, influence can be useful in filteringopinions, information, and data.

It will be appreciated that reputation and influence provide uniqueadvantages in accordance with some embodiments for the ranking ofindividuals or products or services of any type in any means or form.

Accordingly, systems and methods in accordance with some embodiments areprovided allowing for the use of reputation scores and/or influencescores to determine at least in part, in combination with other methodsand systems, the ranking of any subset of individual entities in a givenset of entities; in which the entities include any of the following:documents on the Internet, products, services, data files, legal ornatural persons, or any entities in any form or means that can berepresented within the computer network. The systems and methodsdescribed herein in accordance with some embodiments provide that asubset of the ranked entities are made available based on selectioncriteria, such as rank, date or time, geography/location associated withthe entity, and/or any other selection criteria. The systems and methodsdescribed herein in accordance with some embodiments provide that theinfluence and/or reputation are estimated using any technique, includingbut not limited to the various techniques described herein.

The systems and methods described herein in accordance with someembodiments provide for the use of reputation scores and/or influencescores to determine at least in part, in combination with other methodsand systems, the ranking of any subset of individual entities in a givenset of entities; in which the entities include natural or legal persons,or other entities such as computational processes, documents, datafiles, or any form of product or service or information of any means orform for which a representation has been made. In some embodiments, themeasures of influence and reputation are on dimensions that, forexample, are related to a specific topic (e.g., automobiles orrestaurants), or source (e.g., a weblog or Wikipedia entry or newsarticle or Twitter feed), or search term (e.g., key words or phrasesspecified in order to define a subset of all entities that match thesearch term(s)), in which a subset of the ranked entities are madeavailable based on selection criteria, such as the rank, date or time,or geography/location associated with the entity, and/or any otherselection criteria.

An illustrative implementation of systems and methods described hereinin accordance with some embodiments includes a social graph ofindividuals on the Internet, as shown in FIG. 1, in which theindividuals represent natural or legal persons and the documents (e.g.,#1 and #2) represent natural or legal persons, or other entities such ascomputational processes, documents, data files, or any form of productor service or information of any means or form for which arepresentation has been made within the computer network within thissystem.

In some embodiments, the social graph is directed (e.g., a directedgraph). In some embodiments, the social graph is undirected (e.g., anundirected graph).

In some embodiments, the social graph is explicit, with individualsexpressing a link to other individuals. In some embodiments, the socialgraph is implicit, with techniques for identifying the links betweenindividuals, such as trust, respect, positive, or negative opinion.

In some embodiments, the links or edges of the social graph representdifferent forms of association including, for example, friendship,trust, acquaintance and the edges on the graph are constrained bydimensions representing ad-hoc types including but not limited tosubjects, fields of interest, and/or search terms.

In some embodiments, the nodes of the social graph represent people orother entities (e.g., web pages; authors; reviewers; users ofmicroblogging services, such as Twitter; users of social networks, suchas MySpace or Facebook; bloggers; and/or any other entity) that includeexpressions of opinion, reviews, or other information useful for theestimation of influence, and that each node on the graph represents aninfluential entity, once influence for that node has been, for example,estimated.

In some embodiments, the nodes are placed in two categories, as subjectscapable of having an opinion or making a citation, in which suchexpression is explicit, expressed, implicit, or imputed through anyother technique; and as objects about which subjects have opinions ormake citations; in which, for example, certain objects are alsosubjects; and in which an object has reputation scores indicating thepossibly collective opinion of subjects on the object, and subjects haveinfluence scores indicating the degree to which the subject's opinioninfluences other subjects.

In some embodiments, in which nodes are in two categories as describedabove, the reputation scores of objects are uses for the selection andranking of objects, for example, weighted by the influence scores of thesubjects related to the object, for example, in combination with otherattributes of objects including to semantic or descriptive dataregarding the object. In some embodiments, the subjects include one ormore of the following: users of microblogging services such as Twitter,users of social networks such as MySpace or Facebook, bloggers, andreviewers. In some embodiments, the objects include one or more of thefollowing: Internet web sites, blogs, music, videos, books, films, andother objects that can be represented with, for example, a UniformResource Identifier (URI). In some embodiments, the objects includeentities that are each associated with a Uniform Resource Identifier(URI), wherein the subjects include entities representing authors ofInternet content or users of social media services including one or moreof the following: blogs, Twitter, and reviews on Internet web sites,wherein the links are citations of objects from subjects including oneor more of the following: Tweets, blog posts, and reviews of objects onInternet web sites.

FIG. 2 is a block diagram showing the cooperation of exemplarycomponents of another illustrative implementation in accordance withsome embodiments. In particular, FIG. 2 shows an illustrativeimplementation of exemplary reputation attribution platform 200 inaccordance with some embodiments. As shown in FIG. 2, exemplaryreputation attribution platform 200 includes client computingenvironment 220, client computing environment 225 up to and includingclient computing environment 230, communications network 235, servercomputing environment 260, intelligent reputation engine 250,verification data 240, community data 242, reputation guidelines 245,and reputation histories data 247. Also, as shown in FIG. 2, exemplaryreputation attribution platform 200 includes a plurality of reputationdata (e.g., inputted and/or generated reputation data) 205, 210, and 215which can be displayed, viewed, stored, electronically transmitted,navigated, manipulated, stored, and printed from client computingenvironments 220, 225, and 230, respectively.

In some embodiments, in an illustrative operation, client computingenvironments 220, 225, and 230 can communicate and cooperate with servercomputing environment 260 over communications network 235 to providerequests for and receive reputation data 205, 210, and 215. In theillustrative operation, intelligent reputation engine 250 can operate onserver computing environment 260 to provide one or more instructions toserver computing environment 260 to process requests for reputation data205, 210, and 215 and to electronically communicate reputation data 205,210, and 215 to the requesting client computing environment (e.g.,client computing environment 220, client computing environment 225, orclient computing environment 230). As part of processing requests forreputation data 205, 210, and 215, intelligent reputation engine 250 canutilize a plurality of data comprising verification data 240, communitydata 242, reputation guidelines 245, and/or reputation histories data247. Also, as shown in FIG. 2, client computing environments 220, 225,and 230 are capable of processing content production/sharing data 205,210, and 215 for display and interaction to one or more participatingusers (not shown).

FIG. 3 is a block diagram showing an illustrative block representationof an illustrative system in accordance with some embodiments. Inparticular, FIG. 3 shows a detailed illustrative implementation ofexemplary reputation attribution environment 300 in accordance with someembodiments. As shown in FIG. 3, exemplary content reputationattribution environment 300 includes intelligent reputation platform320, verification data store 315, reputation guidelines data store 310,reputation histories data store 305, community data store 307, usercomputing environment 325, reputation targets (e.g., users) 330,community computing environment 340, and community 345. Additionally, asshown in FIG. 3, reputation attribution environment 300 includesreputation session content 350, which can be displayed, viewed,transmitted and/or printed from user computing environment 325 and/orcommunity computing environment 340.

In some embodiments, in an illustrative implementation, intelligentreputation platform 320 can be electronically coupled to user computingenvironment 325 and community computing environment 340 viacommunications network 335. In some embodiments, communications network335 includes fixed-wire (e.g., wire line) and/or wireless intranets,extranets, and/or the Internet.

In some embodiments, in an illustrative operation, users 330 caninteract with a reputation data interface (not shown) operating on usercomputing environment 325 to provide requests to initiate a reputationsession that are passed across communications network 335 to intelligentreputation platform 320. In the illustrative operation, intelligentreputation platform 320 can process requests for a reputation sessionand cooperate with interactive verification data store 315, reputationguidelines data store 310, reputation histories data store 305, andcommunity data store 307 to generate a reputation session for use byusers 330 and community 345.

In some embodiments, in an illustrative implementation, verificationdata store 315 can include data representative of connections betweenusers 330 and community members 345. Such data can include but is notlimited to connections between users to identify a degree of associationfor use in generation of reputation data. In the illustrativeimplementation, reputation guideline data store 310 can include datarepresentative of one or more rules for attributing reputations amongstusers 330 and community 345. Reputation histories data store 305 caninclude one or more generated reputation attributions for use as part ofreputation data processing. Community data store 307 can include datarepresentative of community feedback for generated reputation data. Forexample, the data representative of connections can be provided throughuser input or generated from any number of techniques including but notlimited to automated or computer-assisted processing of data availableon computer networks, links expressed or implied between entities onsocial networking websites, user commentary or “blogging” websites, orany other form of document available on the Internet.

FIG. 4 is a block diagram describing the interaction of various partiesof an exemplary referral environment in accordance with someembodiments. In particular, FIG. 4 shows contributing elements ofexemplary reputation attribution environment 400 in accordance with someembodiments. As shown, exemplary reputation attribution environment 400comprises a plurality of sub-environments 405, 410, and 415 and numerousreputation targets A-Q. As shown, reputation targets can have directand/or indirect connections with other reputations targets within agiven sub-environment 405, 410, or 415 and/or with other reputationtargets that are outside sub-environments 405, 410, 415.

In some embodiments, in an illustrative implementation, sub-environments405, 410, or 415 can represent one or more facets of a reputationtarget's experience, such as work, home, school, club(s), and/orchurch/temple/commune. In the illustrative implementation, an exemplaryreputation target Q can inquire about the reputation of other reputationtargets (e.g., obtain trusted data for use to assist in making adecision, determine an affinity, and/or identify a dislike). Theindividual reputations of each of the target participants can be derivedaccording to the herein described techniques (e.g., in FIGS. 5 and 6) sothat each reputation target is attributed one or more reputationindicators (e.g., a reputation score associated for restaurantreferrals, another reputation score associated for movie referrals,another reputation score associated for match-making, etc.). Thereputation indicators can be calculated based on the degree and numberof relationships between reputation targets in a given sub-environmentand/or outside of a sub-environment. Once calculated, an exemplaryreputation target Q can query other reputation targets for trusted data(e.g., recommendations and/or referrals) and can process such trusteddata according to reputation score of the data source (e.g., reputationtarget).

For example, sub-environment 405 can represent a place of business,sub-environment 410 can represent home, and sub-environment canrepresent a country club. In some embodiments, in an illustrativeoperation, each of the reputation targets of reputation attributionenvironment 400 can be attributed one or more reputation scores (e.g.,reputation score for business data, reputation score for family data,etc.). In the illustrative operation, the reputation score for eachreputation target for each category (e.g., business, family, social,religious, etc.) can be calculated according to the degree ofrelationship with other reputation targets and/or the number ofconnections with other relationship targets.

In some embodiments, in the illustrative operation, reputation target Qcan request data regarding a business problem (e.g., how to broker atransaction). Responsive to the request, the reputation targets ofsub-environment 405 (e.g., reputation target can act as data sources forreputation target Q) providing data that can satisfy reputation targetQ's request. Additionally, other reputation targets, who are notdirectly part of sub-environment 405, can also act as data sources toreputation target Q. In this context, the reputation score forreputation targets A, B, C, and/or D) can have a higher reputation scorethan other reputation targets not part of sub-environment 405 as suchreputation targets are within sub-environment 405, which is focused onbusiness. In the illustrative operation, other reputation targets notpart of sub-environment 405 can have equal or near level reputationscores to reputation targets (A, B, C, and/or D) of sub-environment 405based on the connections with reputation targets A, B, C, and/or D andreputation target Q. For example, as shown in FIG. 4, reputation targetI can have a relatively high reputation score as it pertains to businessas reputation target I has a number of direct and indirect connections(I-A, I-G-B, I-H-D, I-G-E-D) to reputation targets (e.g., A, B, C,and/or D) of sub-environment 405 and to inquiring reputation target Q.

It is appreciated that although exemplary reputation attributionenvironment 400 of FIG. 4 is shown have a configuration ofsub-environments having various participants, that such description ismerely illustrative the contemplated reputation attribution environmentof the herein described systems and methods can have numeroussub-environments with various participants in various non-describedconfigurations.

FIG. 5 is a block diagram of the search space of an exemplary referralenvironment in accordance with some embodiments. In particular, FIG. 5shows exemplary reputation scoring environment 500 in accordance withsome embodiments. As shown in FIG. 5, reputation scoring environment 500includes a plurality of dimensions 505, 510, and 515, which areoperatively coupled to one or more transitive dimensions 520 and 525.Further, as shown, reputation scoring environment 500 includes one ormore entities 530, 535, 545, 550, 560, and 570 residing on one or moreof dimensions 505, 510, and 515 as well as transitive connectors 540,565, 570, and 580 residing on transitive dimensions 520 and 525.

In some embodiments, in an illustrative operation, scores for one ormore entities 530, 535, 545, 550, 560 and/or 570 can be determined on anetwork (not shown) on a given dimension 505, 510 and/or 515. In theillustrative operation, an entity 530, 535, 545, 550, 560 and/or 570 canbe directly linked to any number of other entities 530, 535, 545, 550,560 and/or 570 on any number of dimensions 505, 510, and/or 515 (e.g.,such that each link, direct or indirect link, can be associated with ascore). For example, one or more dimension 505, 510, and/or 515 can havean associated one or more transitive dimension 520 and/or 525.

In the illustrative operation, a directed path 507 on a given dimension505 between two entities 530 and 535, a source and a target, includes adirected link from the source entity 530 (e.g., illustratively 530 asall entities 530, 535, 545, 550, 560, and/or 570 can be source and/ortarget entities depending on the perspective of the scoring attributionplatform as described herein in accordance with various embodiments) toan intermediate entity 540, prefixed to a directed path from theintermediate entity 540 to the target entity 535.

In some embodiments, in an illustrative implementation, links on thepath can be on one or more transitive dimensions 520 and/or 525associated with a given dimension 505, 510, and/or 515. For example, todetermine a score on a given dimension 505, 510, and/or 515 between asource entity 530 and a target entity 535, directed paths 507 on thegiven dimension 505, 510, and/or 515 can be determined through any kindof graph search (not shown). In the illustrative operation, theindividual scores on the one or more links on the one or more paths canbe combined to produce one or more resulting scores using varioustechniques for propagating scores and for resolving conflicts betweendifferent scores. For example, one or more intermediate entities 540,565, 570, and/or 580 can also be provided with a measure of influence onthe dimensions 505, 510 and/or 515 based on the universe of sourceentities (e.g., 530, 535, 545, 550, 560, 570), the universe of targetentities (e.g., 530, 535, 545, 550, 560, 570) and the links betweenthem.

It is appreciated that although reputation scoring environment 500 isshown to have a particular configuration operating to an illustrativeoperation with a particular number of dimensions, transitive dimensions,entities, direct connections and indirect connections that suchdescription is merely illustrative as the influence calculation withinthe herein described techniques can employ various dimensions,transitive dimensions, entities, direct, and/or indirect connectionshaving various configurations and assemblages operating according toother illustrative operations.

FIG. 6 is a flow diagram showing illustrative processing performed ingenerating referrals in accordance with some embodiments. In particular,FIG. 6 shows exemplary processing in calculating reputations scores inaccordance with some embodiments. As shown in FIG. 6, processing beginsat block 600 at which a population of entities are identified. Fromthere processing proceeds to block 605 at which selected constraints areestablished on the identified population such that theinterrelationships between the entities can be mapped to values −1 to +1for a target entity connected to source entity. Processing then proceedsto block 610 at which entity relationships are represented as a directedgraph on a given dimension such that an entity can be directly,uni-directionally linked to any number of other entities on any numberof dimensions with each direct link having an associated score within aselected range R such that each dimension can have therewith anassociated transitive dimension. From there, processing proceeds toblock 615 at which a graph search is performed to identify directedpaths from a source entity to a target entity on a given dimension togenerate a global directed graph having combinations of availableidentified directed paths and to generate a scoring graph for identifieddirected paths. Processing then proceeds to block 620 at whichindividual scores of the direct links on an identified path can becombined to generate one or more final scores (e.g., reputation score)for a target entity from the perspective of a source entity.

In some embodiments, in an illustrative implementation, the processingof FIG. 6 can be performed such that for a population of entities, amethod of determining scores, each within the range R which can bemapped to the values −1 . . . +1, for a target entity connected to asource entity on a network that can be conceptually represented as adirected graph on each given dimension, such that an entity can bedirectly, uni-directionally linked to any number of other entities onany number of dimensions, with each direct link having an associatedscore within the range R. Further, each dimension can have an associatedtransitive dimension and such that a directed path on a given dimensionbetween two entities, a source entity and a target entity, can bedefined as a direct link from the source entity to an intermediateentity, prefixed to a directed path from the intermediate entity to thetarget entity, subject to the selected constraints including but notlimited to: 1) a direct link from any entity to the target entity mustbe on the given dimension, and 2) a direct link on the path from anyentity to an intermediate entity that is not the target entity must beeither on the transitive dimension associated with the given dimension,or on the given dimension itself if the given dimension is itself is atransitive dimension.

Furthermore, in the illustrative operation, the processing of FIG. 6 caninclude but is not limited to: (A) performing a graph search (e.g.,using various graph search techniques) to identify directed paths from asource entity to a target entity on a given dimension subject to theabove definition of a directed path that, for example, optimally resultsin a directed graph combining all such identified directed paths. Theresulting directed graph, for example, provides a scoring graph that canbe stored separately. In the illustrative operation, individual scorescan be combined (B) on each direct link on each path on the scoringgraph to produce one or more final scores, with or without an associatedset of confidence values in the range C=0 . . . 1 for each resultingscore, for the target entity from the perspective of the source entity.In the illustrative operation, the acts (A) and (B) can be performed,for example, in sequence, or performed simultaneously; when performedsimultaneously, the combination of individual scores described in act(B) being performed during the graph search described in act (A) withoutthe creation of separately stored scoring graph; and wherein the graphsearch performed in act (A) can be optimized by some combination ofscores identified through act (B) such that the optimization may resultin the exclusion of certain paths between the source entity and thetarget entity.

In some embodiments, in an illustrative operation of the hereindescribed techniques using various systems and methods, the influence ofeach entity can, for example, be estimated as the count of otherentities with direct links to the entity or with a path, possibly with apredefined maximum length, to the entity; with or without the countbeing adjusted by the possible weights on each link, the length of eachpath, and the level of each entity on each path.

In some embodiments, in an illustrative operation of the hereindescribed techniques using various systems and methods, the influence ofeach entity can be estimated with the adjusted count calculated throughthe operations described herein, transformed into a rank or percentilerelative to the similarly measured influence of all other entities.

In some embodiments, in an illustrative operation of the hereindescribed techniques using various systems and methods, the influence ofeach entity can be estimated as the count of actual requests for data,opinion, or searches relating to or originating from other entities,entities with direct links to the entity or with a path, possibly with apredefined maximum length, to the entity; such actual requests beingcounted if they result in the use of the paths originating from theentity (e.g., representing opinions, reviews, citations or other formsof expression) with or without the count being adjusted by the possibleweights on each link, the length of each path, and the level of eachentity on each path.

In some embodiments, in an illustrative operation of the hereindescribed techniques using various systems and methods, the influence ofeach entity can be estimated as the count of actual requests for data,opinion, or searches relating to or originating from other entities,entities with direct links to the entity or with a path, possibly with apredefined maximum length, to the entity; such actual requests beingcounted if they occur within a predefined period of time and result inthe use of the paths originating from the entity (e.g., representingopinions, reviews, citations or other forms of expression) with orwithout the count being adjusted by the possible weights on each link,the length of each path, and the level of each entity on each path.

In some embodiments, in an illustrative operation of the hereindescribed techniques using various systems and methods, the influence ofeach entity can be estimated with the adjusted count calculated throughthe operations described herein, transformed into a rank or percentilerelative to the similarly measured influence of all other entities.

In some embodiments, in an illustrative operation of the hereindescribed techniques using various systems and methods, the influence ofeach entity can be estimated by applying to it any of several graphmetric functions, such as centrality or betweenness, in which thefunctions, such as centrality or betweenness, can be estimated either byrelating the entity to the entire graph including all linked entities,or by relating the entity to a subgraph including all entities linked tothe entities directly or by paths of up to a given length.

In some embodiments, the illustrative operations described herein forthe calculation of influence can be performed for each dimensionseparately, resulting in one influence measure for each entity for eachdimension; for all dimensions together, resulting in one influencemeasure for each entity; or for any given subgroup of dimensionstogether applied to any given entity, resulting in each entity having asmany influence measures as the number of subgroups of dimensions appliedto that entity.

In some embodiments, in an illustrative operation of the hereindescribed techniques using various systems and methods, the influence ofeach entity as estimated in each of the operations described herein, canbe adjusted by metrics relating to the graph comprising all entities ora subset of all linked entities. For example, such metrics can includethe density of the graph, defined as the ratio of the number of links tothe number of linked entities in the graph; such metrics can betransformed by mathematical functions optimal to the topology of thegraph, especially where it is known that the distribution of links amongentities in a given graph may be non-linear. An example of such anadjustment would be the operation of estimating the influence of anentity as the number of directed links connecting to the entity, dividedby the logarithm of the density of the graph comprising all linkedentities. Such an operation can provide an optimal method of estimatinginfluence rapidly with a limited degree of computational complexity.

In some embodiments, in an illustrative operation of the hereindescribed techniques using various systems and methods, in which theinfluence of entities as estimated in each of the operations describedherein can be estimated for separate, unconnected graphs; and for whichsuch influence estimated for entities in separate, unconnected graphscan be adjusted by applying metrics relating to each separateunconnected graph in its entirety, as shown in the operation describedherein; the influence of each entity on one graph, thus adjusted, can benormalized and compared to the influence of another entity on anothergraph, also thus adjusted. For example, such an operation allows for theuse of influence measures across separate, unconnected graphs.

In some embodiments, in an illustrative operation of the hereindescribed techniques using various systems and methods, the estimationof influence can be optimized for different contexts and requirements ofperformance, memory, graph topology, number of entities, etc., by anycombination of the operations described above in paragraphs above, andany similar operations involving metrics including but not limited tovalues comprising: the number of potential source entities to the entityfor which influence is to be estimated, the number of potential targetentities, the number of potential directed paths between any one entityand any other entity on any or all given dimensions, the number ofpotential directed paths that include the entity, the number of timeswithin a defined period that a directed link from the entity is used fora scoring, search or other operation.

In some embodiments, in an illustrative implementation of this systemapplied to the World Wide Web and documents, data and information on theInternet, such data and information are modeled (as shown in FIG. 1) asdocuments, or objects, and authors, or subjects, in which subjects arerepresentation of any entities that make citations, in which citationsinclude the expression of opinions on other subjects or objects,expressions of authors in the form of postings to blogs, Wikipediaentries, postings to social media such as Twitter or Jaiku, postings towebsites, postings in the form of reviews, recommendations, or any otherform of citation made to mailing lists, newsgroups, discussion forums,comments to websites or any other form of Internet publication; in whichcitations by one subject regarding another subject, such as arecommendation of one author by another, is treated as representing anexpression of trustworthiness, for example, limited to category orcategories identified or imputed, and citations by one subject regardingan object, such as a recommendation of a website, or a restaurantreview, is treated as representation an expression of opinion ordescription; in which citations are treated as edges on a network wheresubjects and objects form nodes; and where reputation is determined forsubjects and objects and influence is determined for subjects and whereobjects are be identified, selected and ranked based on the reputationascribed to them or the influence of subjects citing objects or the dateof the citations or the content of citations matching search terms orthe content of documents matching search terms, or any computation ofrank and selection based on a function of any combination of thesevariables and additional variables.

In some embodiments, in an illustrative implementation of the techniquesdescribed herein is implemented as follows; in which users can providetext, referred to here as “search terms”, and for every objectrepresented, such as by a Uniform Resource Locator (“URL”), calculationsare made for all represented citations of that object in order todetermine semantic weights such as term frequency-inverse documentfrequency (“TF-IDF”), as well as the influence of the author of eachsuch citation of each object, and a function of the semantic weight andinfluence of all citations of each object results in a score for theobject on the basis of which a ranking of objects is performed and aranked list of objects is presented to the user; and where the score isused as a threshold for the selection of objects, and where otherranking criteria (e.g., first, last, modal, median date of citations ofthe object) is used in some combination with the score.

In some embodiments, in an illustrative implementation of the techniquesdescribed herein provides for the selection score and object ranking asa combination of not only the citation score for objects and theinfluence score for subjects citing them, but an expertise score foreach subject citing each such object, based on the citations from eachsubject matching descriptive criteria (“search terms” or ontologicallysimilar terms) as a relative share of all citations from that subject,and citations from all subjects matching above-described descriptivecriteria as a relative share of citations from all subjects.

In some embodiments, in an illustrative implementation of the techniquesdescribed herein provides for the selection score and object ranking asa combination of not only the citation score for objects and thereputation score for subjects citing them, but an expertise score foreach subject citing each such object, based on the citations from eachsubject matching descriptive criteria (“search terms” or ontologicallysimilar terms) as a relative share of all citations from that subject,and citations from all subjects matching above-described descriptivecriteria as a relative share of citations from all subjects.

It is understood that the herein described techniques using varioussystems and methods are susceptible to various modifications andalternative constructions. There is no intention to limit the hereindescribed systems and methods to the specific constructions describedherein. On the contrary, the herein described systems and methods areintended to cover all modifications, alternative constructions, andequivalents falling within the scope and spirit of the herein describedtechniques using, for example, various systems and methods.

It should also be noted that the herein described systems and methodscan be implemented in a variety of electronic environments (includingboth non-wireless and wireless computer environments, including cellphones and video phones), partial computing environments, and real worldenvironments. The various techniques described herein may be implementedin hardware or software, or a combination of both. In some embodiments,the techniques are implemented in computing environments maintainingprogrammable computers that include a computer network, processor,servers, and a storage medium readable by the processor (includingvolatile and non-volatile memory and/or storage elements), at least oneinput device, and at least one output device. Computing hardware logiccooperating with various instructions sets are applied to data toperform the functions described above and to generate outputinformation. The output information is applied to one or more outputdevices. For example, programs used by the exemplary computing hardwarecan be implemented in various programming languages, including highlevel procedural or object oriented programming language to communicatewith a computer system. Illustratively the herein described techniquesusing various apparatus and methods may be implemented in assembly ormachine language, if desired. In any case, the language may be acompiled or interpreted language. In some embodiments, each suchcomputer program can be stored on a storage medium or device (e.g., ROMor magnetic disk) that is readable by a general or special purposeprogrammable computer for configuring and operating the computer whenthe storage medium or device is read by the computer to perform theprocedures described above. In some embodiments, the apparatus may alsobe considered to be implemented as a computer-readable storage medium,configured with a computer program, where the storage medium soconfigured causes a computer to operate in a specific and predefinedmanner.

Although the foregoing embodiments have been described in some detailfor purposes of clarity of understanding, the invention is not limitedto the details provided. There are many alternative ways of implementingthe invention. The disclosed embodiments are illustrative and notrestrictive.

What is claimed is:
 1. A method, comprising: receiving a graph, with asubset of the graph being a subject graph of subject nodes that includesat least one or more subjects linked to one or more other subjects, thegraph including one or more objects each linked to one or more subjectsin the subject graph, each subject being a user, each object being acontent item, and each edge in the graph representing a citation andeach citation representing an expression of opinion or description by asubject on an object; determining reputation scores for one or moresubjects based on connections among the subjects in the graph, whereinthe reputation scores indicate the reputation of the subjects; selectingonly a subset of citations for each object from among all the citationsciting each object, the content of the citations in the selected subsetmatching one or more of the search terms for the search query, adifferent subset of citations being selectable for a same object when adifferent search query is provided; assigning citation scores to anysubset of a plurality of objects, the citation scores indicatingrelevance of the objects cited by citations and are determined based atleast in part on matching one or more search terms with the content ofthe citations of the objects by the one or more subjects, the selectionscores for an object computed for each search query based on a subset ofsubjects citing the object, with the subjects in the subset being thesubjects of previously selected subsets of citations to each object;combining the citation scores for the objects and the reputation scoresfor the subjects citing the objects to calculate selection scores forthe objects determined based on matching the one or more search termswith the content of the citations, the selection scores for an objectcomputed for each search query based on a subset of subjects citing theobject, with the subjects in the subset being the subjects of previouslyselected subsets of citations to each object, a different selectionscore computed for the same object when a different search query isprovided; and selecting and ranking the objects based on the selectionscores of the objects, a different ranking computed for a same set oroverlapping sets of objects when the search query is different.
 2. Themethod recited in claim 1, wherein the objects include books, films,music, documents, websites, objects for sale, objects that are reviewedor recommended or cited, or any entities that are associated with aUniform Resource Identifier (URI), wherein the subjects include entitiesrepresenting authors of Internet content or users of social mediaservices including one or more of the following: blogs, Twitter, andreviews on Internet web sites, wherein the links are citations ofobjects from subjects including one or more of the following: Tweets,blog posts, and reviews of objects on Internet web sites.
 3. The methodrecited in claim 1, further comprising: receiving descriptive criteriafrom a user.
 4. The method recited in claim 1, further comprising:receiving search terms, wherein the search terms are provided in asearch query, and wherein the search terms are included in thedescriptive criteria.
 5. The method recited in claim 1, furthercomprising: displaying a subset of top ranked objects based on theselection scores.
 6. The method recited in claim 1, further comprising:receiving search terms, wherein the search terms are provided in asearch query; and displaying a subset of top ranked objects based on theselection scores, wherein the top ranked objects based on the selectionscores provide a subjective based search result.
 7. The method recitedin claim 1, further comprising: determining a content score for anobject based on the description criteria; wherein the selection score isbased on the citation score, the subjective reputation score, and thecontent score.
 8. The method recited in claim 1, further comprising:determining an expertise score for each subject citing each object basedon a second descriptive criteria, wherein the selection score is basedon the citation score, the subjective reputation score, and theexpertise score.
 9. A computer-implemented method, comprising: receivinga graph, with a subset of the graph being a subject graph of subjectnodes that includes at least one or more subjects linked to one or moreother subjects, the graph including one or more objects each linked toone or more subjects in the subject graph, each subject being a user,each object being a content item, and each edge in the graphrepresenting a citation and each citation representing an expression ofopinion or description by a subject on an object; determining reputationscores for a first dimension for one or more objects from a perspectiveof a first subject based on connections among the subjects in the graph,wherein the reputation scores indicate the reputation of the subjects;and selecting only a subset of citations for each object from among allthe citations citing each object, the content of the citations in theselected subset matching one or more of the search terms for the searchquery, a different subset of citations being selectable for a sameobject when a different search query is provided; assigning citationscores to any subset of a plurality of objects, wherein the citationscores indicate relevance of the objects cited by citations and aredetermined based at least in part on matching of one or more searchterms with the content of the citations of the objects by the one ormore subjects, the selection scores for an object computed for eachsearch query based on a subset of subjects citing the object, with thesubjects in the subset being the subjects of previously selected subsetsof citations to each object; combining the citation scores for theobjects and the reputation scores for the subjects citing the objects tocalculate selection scores for the objects determined based on matchingof the one or more search terms with the content of the citations, theselection scores for an object computed for each search query based on asubset of subjects citing the object, with the subjects in the subsetbeing the subjects of previously selected subsets of citations to eachobject, a different selection score computed for the same object when adifferent search query is provided; selecting and ranking the objectsbased on the reputation scores and matching of one or more search termswith the content of the citations citing the objects, a differentranking computed for a same set or overlapping sets of objects when thesearch query is different; and displaying the ranked objects as searchresult to a user or providing the search result in machine readableform.
 10. The method recited in claim 9, wherein the objects includebooks, films, music, documents, websites, objects for sale, objects thatare reviewed or recommended or cited, or any entities that areassociated with a Uniform Resource Identifier (URI), wherein thesubjects include entities representing Internet authors or users ofsocial media services including one or more of the following: blogs,Twitter, and reviews on Internet web sites, wherein the links arecitations of the objects from the subjects including one or more of thefollowing: Tweets, Blog posts, and reviews of objects on Internet websites.
 11. The method recited in claim 9, wherein the first dimensionincludes search terms.
 12. The method recited in claim 9, furthercomprising: receiving descriptive criteria.
 13. The method recited inclaim 9, further comprising: receiving search terms, wherein the searchterms are provided in a search query, and wherein the search terms areincluded in the descriptive criteria.
 14. The method recited in claim 9,further comprising: displaying a subset of top ranked objects based onthe reputation scores.
 15. The method recited in claim 9, furthercomprising: receiving search terms, wherein the search terms areprovided in a search query; and displaying a subset of top rankedobjects based on the reputation scores, wherein the top ranked objectsbased on the reputation scores provide a subjective based search result.16. The method recited in claim 9, further comprising: determining acontent score for one or more objects based on the description criteria;wherein a selection score for each object is based on the subjectivereputation score and the content score; and ranking the one or moreobjects based on the selection score for each object.
 17. Acomputer-implemented method, comprising: receiving a graph, wherein asubset of the graph is a subject graph of subject nodes that includes atleast one or more subjects linked to one or more other subjects, andwherein the graph includes one or more objects, wherein each linked toone or more subjects in the subject graph, wherein each subject is auser, and each object is a content item, and wherein each edge in thegraph represents a citation, wherein each citation represents anexpression of opinion or description by a subject on an object;determining influence scores for one or more subjects based onconnections among the subjects in the graph, wherein the reputationscores indicate the reputation of the subjects; selecting only a subsetof citations for each object from among all the citations citing eachobject, where a different subset of citations being selectable for asame object when a different search query is provided; assigningcitation scores to any subset of a plurality of the objects from theperspective of any subjects, wherein the citation score indicaterelevance of the objects cited by citations and are based at least inpart on matching of one or more search terms with the content of thecitations of the objects by the one or more subjects; wherein thecitation scores for an object are computed for each search query as theyare computed based on the selected subset of citations for the objectwhere the content of the citations in the subset matches one or more ofthe search terms for the search query; combining the citation scores forthe objects and the influence scores for the subjects citing thoseobjects to calculate selection scores for the objects determined basedon matching of the one or more search terms with the content of thecitations; wherein the selection scores for an object are computed foreach search query as they are computed based on a subset of subjectsciting the object where the subjects in the subset are comprised of thesubjects of the previously selected subsets of citations to each object;and selecting and ranking the objects based on the selection scores ofthe objects; and displaying the ranked objects as search result to auser or providing the search result in machine readable form.
 18. Themethod recited in claim 17, wherein the objects include books, films,music, documents, websites, objects for sale, objects that are reviewedor recommended or cited, or any entities that are associated with aUniform Resource Identifier (URI), wherein the subjects include entitiesrepresenting Internet authors or users of social media servicesincluding one or more of the following: blogs, Twitter, and reviews onInternet web sites, wherein the links are citations of the objects fromthe subjects including one or more of the following: Tweets, Blog posts,and reviews of objects on Internet web sites.
 19. The method recited inclaim 17, further comprising: receiving descriptive criteria.
 20. Themethod recited in claim 17, further comprising: receiving search terms,wherein the search terms are provided in a search query, and wherein thesearch terms are included in the descriptive criteria.
 21. The methodrecited in claim 17, further comprising: displaying a subset of topranked objects based on the selection scores, wherein the top rankedobjects based on the selection scores provide an influence based searchresult.
 22. The method recited in claim 17, further comprising:determining a content score for a first object based on the descriptioncriteria; wherein the selection score is based on the citation score,the influence score, and the content score.
 23. The method recited inclaim 17, further comprising: determining an expertise score for eachsubject citing each object based on a second descriptive criteria,wherein the selection score is based on the citation score, theinfluence score, and the expertise score.
 24. The method recited inclaim 17, further comprising: determining a content score for a firstobject based on a first description criteria; wherein the selectionscore is based on the citation score, the influence score, and thecontent score; and determining an expertise score for each subjectciting each object based on a second descriptive criteria, wherein theselection score is based on the citation score, the influence score, andthe expertise score, and wherein the second descriptive criteriaincludes the first descriptive criteria.
 25. The method recited in claim17, wherein the selection score and object ranking is a combination ofthe citation score for objects and the influence score for subjectsciting to the objects and an expertise score for each subject citingeach object based on the citations from each subject matchingdescriptive criteria as a relative share of all citations from thesubject, and citations from all subjects matching the descriptivecriteria as a relative share of citations from all subjects.
 26. Themethod recited in claim 17, wherein the selection score and objectranking is a combination of the citation score for objects and thereputation score for subjects citing to the object and an expertisescore for each subject citing each object based on the citations fromeach subject matching descriptive criteria as a relative share of allcitations from the subject and citations from all subjects matching thedescriptive criteria as a relative share of citations from all subjects.27. The method recited in claim 17, wherein search terms include one ormore of the following search criteria: geography, language, and time.